{
 "cells": [
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T10:04:46.643284Z",
     "start_time": "2025-06-12T10:00:55.286434Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import random\n",
    "import numpy as np\n",
    "\n",
    "# 固定随机种子\n",
    "def set_seed(seed=42):\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    if torch.cuda.is_available():\n",
    "        torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "set_seed()\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# =================== 数据加载 ===================\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64,\n",
    "                                          shuffle=True)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=100,\n",
    "                                         shuffle=False)\n",
    "\n",
    "# =================== 模型定义 ===================\n",
    "\n",
    "# 1. MLP模型\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MLP, self).__init__()\n",
    "        self.fc1 = nn.Linear(32 * 32 * 3, 512)\n",
    "        self.fc2 = nn.Linear(512, 256)\n",
    "        self.fc3 = nn.Linear(256, 10)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 32 * 32 * 3)\n",
    "        x = self.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = self.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "# 2. LSTM模型\n",
    "class LSTMClassifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LSTMClassifier, self).__init__()\n",
    "        self.lstm = nn.LSTM(input_size=32 * 3, hidden_size=128, num_layers=2,\n",
    "                            batch_first=True, dropout=0.3)\n",
    "        self.fc = nn.Linear(128, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 32, 32 * 3)\n",
    "        output, (hn, cn) = self.lstm(x)\n",
    "        out = self.fc(output[:, -1, :])\n",
    "        return out\n",
    "\n",
    "# 3. CNN模型\n",
    "class SimpleCNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleCNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)\n",
    "        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc1 = nn.Linear(64 * 8 * 8, 256)\n",
    "        self.fc2 = nn.Linear(256, 10)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(self.relu(self.conv1(x)))\n",
    "        x = self.pool(self.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 64 * 8 * 8)\n",
    "        x = self.dropout(self.relu(self.fc1(x)))\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "# =================== 训练与测试函数 ===================\n",
    "\n",
    "def train_model(model, trainloader, epochs=10):\n",
    "    model.to(device)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "\n",
    "    for epoch in range(epochs):\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "        for images, labels in trainloader:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "\n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "\n",
    "            running_loss += loss.item()\n",
    "        print(f\"[Epoch {epoch + 1}] Loss: {running_loss / len(trainloader):.4f}\")\n",
    "\n",
    "def test_model(model, testloader):\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    with torch.no_grad():\n",
    "        for images, labels in testloader:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = model(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "    acc = 100 * correct / total\n",
    "    print(f\"Test Accuracy: {acc:.2f}%\")\n",
    "    return acc\n",
    "\n",
    "# =================== 主流程 ===================\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    results = {}\n",
    "\n",
    "    print(\"\\n Training MLP...\")\n",
    "    mlp_model = MLP()\n",
    "    train_model(mlp_model, trainloader)\n",
    "    results[\"MLP\"] = test_model(mlp_model, testloader)\n",
    "\n",
    "    print(\"\\n[表情] Training LSTM...\")\n",
    "    lstm_model = LSTMClassifier()\n",
    "    train_model(lstm_model, trainloader)\n",
    "    results[\"LSTM\"] = test_model(lstm_model, testloader)\n",
    "\n",
    "    print(\"\\n Training CNN...\")\n",
    "    cnn_model = SimpleCNN()\n",
    "    train_model(cnn_model, trainloader)\n",
    "    results[\"CNN\"] = test_model(cnn_model, testloader)\n",
    "\n",
    "    print(\"\\n=====  模型准确率对比 =====\")\n",
    "    for model_name, acc in results.items():\n",
    "        print(f\"{model_name:<5}: {acc:.2f}%\")\n"
   ],
   "id": "b866772c182bddd2",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n",
      "\n",
      "[表情] Training MLP...\n",
      "[Epoch 1] Loss: 1.7490\n",
      "[Epoch 2] Loss: 1.5993\n",
      "[Epoch 3] Loss: 1.5382\n",
      "[Epoch 4] Loss: 1.4986\n",
      "[Epoch 5] Loss: 1.4711\n",
      "[Epoch 6] Loss: 1.4309\n",
      "[Epoch 7] Loss: 1.4057\n",
      "[Epoch 8] Loss: 1.3858\n",
      "[Epoch 9] Loss: 1.3665\n",
      "[Epoch 10] Loss: 1.3452\n",
      "Test Accuracy: 51.60%\n",
      "\n",
      "[表情] Training LSTM...\n",
      "[Epoch 1] Loss: 1.7678\n",
      "[Epoch 2] Loss: 1.5358\n",
      "[Epoch 3] Loss: 1.4187\n",
      "[Epoch 4] Loss: 1.3313\n",
      "[Epoch 5] Loss: 1.2557\n",
      "[Epoch 6] Loss: 1.1904\n",
      "[Epoch 7] Loss: 1.1292\n",
      "[Epoch 8] Loss: 1.0716\n",
      "[Epoch 9] Loss: 1.0184\n",
      "[Epoch 10] Loss: 0.9662\n",
      "Test Accuracy: 56.06%\n",
      "\n",
      "[表情] Training CNN...\n",
      "[Epoch 1] Loss: 1.4589\n",
      "[Epoch 2] Loss: 1.1045\n",
      "[Epoch 3] Loss: 0.9665\n",
      "[Epoch 4] Loss: 0.8690\n",
      "[Epoch 5] Loss: 0.7923\n",
      "[Epoch 6] Loss: 0.7340\n",
      "[Epoch 7] Loss: 0.6722\n",
      "[Epoch 8] Loss: 0.6260\n",
      "[Epoch 9] Loss: 0.5772\n",
      "[Epoch 10] Loss: 0.5357\n",
      "Test Accuracy: 73.28%\n",
      "\n",
      "===== [表情] 模型准确率对比 =====\n",
      "MLP  : 51.60%\n",
      "LSTM : 56.06%\n",
      "CNN  : 73.28%\n"
     ]
    }
   ],
   "execution_count": 1
  },
  {
   "metadata": {
    "ExecuteTime": {
     "end_time": "2025-06-12T10:24:34.196114Z",
     "start_time": "2025-06-12T10:19:28.142568Z"
    }
   },
   "cell_type": "code",
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.optim as optim\n",
    "import torchvision\n",
    "import torchvision.transforms as transforms\n",
    "import random\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn.metrics import confusion_matrix, classification_report\n",
    "from torch.utils.data import DataLoader, TensorDataset, random_split\n",
    "import pandas as pd\n",
    "import time\n",
    "\n",
    "# 固定随机种子\n",
    "def set_seed(seed=42):\n",
    "    random.seed(seed)\n",
    "    np.random.seed(seed)\n",
    "    torch.manual_seed(seed)\n",
    "    if torch.cuda.is_available():\n",
    "        torch.cuda.manual_seed_all(seed)\n",
    "\n",
    "set_seed()\n",
    "\n",
    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
    "print(f\"Using device: {device}\")\n",
    "\n",
    "# =================== 数据加载 ===================\n",
    "transform = transforms.Compose([\n",
    "    transforms.ToTensor(),\n",
    "    transforms.Normalize((0.5,), (0.5,))\n",
    "])\n",
    "\n",
    "trainset = torchvision.datasets.CIFAR10(root='./data', train=True,\n",
    "                                        download=True, transform=transform)\n",
    "trainloader = torch.utils.data.DataLoader(trainset, batch_size=64,\n",
    "                                          shuffle=True)\n",
    "\n",
    "testset = torchvision.datasets.CIFAR10(root='./data', train=False,\n",
    "                                       download=True, transform=transform)\n",
    "testloader = torch.utils.data.DataLoader(testset, batch_size=100,\n",
    "                                         shuffle=False)\n",
    "\n",
    "# 可视化样本\n",
    "def visualize_samples():\n",
    "    classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
    "    dataiter = iter(trainloader)\n",
    "    images, labels = next(dataiter)\n",
    "    \n",
    "    plt.figure(figsize=(10, 4))\n",
    "    for i in range(8):\n",
    "        plt.subplot(2, 4, i+1)\n",
    "        img = images[i].numpy().transpose((1, 2, 0))\n",
    "        mean = np.array([0.5, 0.5, 0.5])\n",
    "        std = np.array([0.5, 0.5, 0.5])\n",
    "        img = std * img + mean\n",
    "        img = np.clip(img, 0, 1)\n",
    "        plt.imshow(img)\n",
    "        plt.title(classes[labels[i]])\n",
    "        plt.axis('off')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "visualize_samples()\n",
    "\n",
    "# =================== 模型定义 ===================\n",
    "\n",
    "# 1. MLP模型\n",
    "class MLP(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(MLP, self).__init__()\n",
    "        self.fc1 = nn.Linear(32 * 32 * 3, 512)\n",
    "        self.fc2 = nn.Linear(512, 256)\n",
    "        self.fc3 = nn.Linear(256, 10)\n",
    "        self.relu = nn.ReLU()\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 32 * 32 * 3)\n",
    "        x = self.relu(self.fc1(x))\n",
    "        x = self.dropout(x)\n",
    "        x = self.relu(self.fc2(x))\n",
    "        x = self.fc3(x)\n",
    "        return x\n",
    "\n",
    "# 2. LSTM模型\n",
    "class LSTMClassifier(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(LSTMClassifier, self).__init__()\n",
    "        self.lstm = nn.LSTM(input_size=32 * 3, hidden_size=128, num_layers=2,\n",
    "                            batch_first=True, dropout=0.3)\n",
    "        self.fc = nn.Linear(128, 10)\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = x.view(-1, 32, 32 * 3)\n",
    "        output, (hn, cn) = self.lstm(x)\n",
    "        out = self.fc(output[:, -1, :])\n",
    "        return out\n",
    "\n",
    "# 3. CNN模型\n",
    "class SimpleCNN(nn.Module):\n",
    "    def __init__(self):\n",
    "        super(SimpleCNN, self).__init__()\n",
    "        self.conv1 = nn.Conv2d(3, 32, 3, padding=1)\n",
    "        self.conv2 = nn.Conv2d(32, 64, 3, padding=1)\n",
    "        self.pool = nn.MaxPool2d(2, 2)\n",
    "        self.dropout = nn.Dropout(0.5)\n",
    "        self.fc1 = nn.Linear(64 * 8 * 8, 256)\n",
    "        self.fc2 = nn.Linear(256, 10)\n",
    "        self.relu = nn.ReLU()\n",
    "\n",
    "    def forward(self, x):\n",
    "        x = self.pool(self.relu(self.conv1(x)))\n",
    "        x = self.pool(self.relu(self.conv2(x)))\n",
    "        x = x.view(-1, 64 * 8 * 8)\n",
    "        x = self.dropout(self.relu(self.fc1(x)))\n",
    "        x = self.fc2(x)\n",
    "        return x\n",
    "\n",
    "# =================== 训练与测试函数 ===================\n",
    "\n",
    "def train_model(model, trainloader, epochs=10):\n",
    "    model.to(device)\n",
    "    criterion = nn.CrossEntropyLoss()\n",
    "    optimizer = optim.Adam(model.parameters(), lr=0.001)\n",
    "    \n",
    "    # 记录训练历史\n",
    "    train_losses = []\n",
    "    val_losses = []\n",
    "    train_acc = []\n",
    "    val_acc = []\n",
    "    \n",
    "    # 分割验证集\n",
    "    val_size = int(len(trainset) * 0.2)\n",
    "    train_size = len(trainset) - val_size\n",
    "    train_dataset, val_dataset = torch.utils.data.random_split(\n",
    "        trainset, [train_size, val_size])\n",
    "    \n",
    "    train_loader = DataLoader(train_dataset, batch_size=64, shuffle=True)\n",
    "    val_loader = DataLoader(val_dataset, batch_size=64, shuffle=False)\n",
    "    \n",
    "    print(f\"训练集大小: {len(train_dataset)}, 验证集大小: {len(val_dataset)}\")\n",
    "    \n",
    "    best_val_acc = 0.0\n",
    "    best_model = model.state_dict()\n",
    "    patience = 3\n",
    "    counter = 0\n",
    "    \n",
    "    for epoch in range(epochs):\n",
    "        # 训练阶段\n",
    "        model.train()\n",
    "        running_loss = 0.0\n",
    "        correct = 0\n",
    "        total = 0\n",
    "        \n",
    "        for images, labels in train_loader:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            \n",
    "            optimizer.zero_grad()\n",
    "            outputs = model(images)\n",
    "            loss = criterion(outputs, labels)\n",
    "            loss.backward()\n",
    "            optimizer.step()\n",
    "            \n",
    "            running_loss += loss.item()\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        train_loss = running_loss / len(train_loader)\n",
    "        train_accuracy = 100 * correct / total\n",
    "        train_losses.append(train_loss)\n",
    "        train_acc.append(train_accuracy)\n",
    "        \n",
    "        # 验证阶段\n",
    "        model.eval()\n",
    "        val_loss = 0.0\n",
    "        val_correct = 0\n",
    "        val_total = 0\n",
    "        \n",
    "        with torch.no_grad():\n",
    "            for images, labels in val_loader:\n",
    "                images, labels = images.to(device), labels.to(device)\n",
    "                outputs = model(images)\n",
    "                loss = criterion(outputs, labels)\n",
    "                \n",
    "                val_loss += loss.item()\n",
    "                _, predicted = torch.max(outputs.data, 1)\n",
    "                val_total += labels.size(0)\n",
    "                val_correct += (predicted == labels).sum().item()\n",
    "        \n",
    "        val_loss = val_loss / len(val_loader)\n",
    "        val_accuracy = 100 * val_correct / val_total\n",
    "        val_losses.append(val_loss)\n",
    "        val_acc.append(val_accuracy)\n",
    "        \n",
    "        print(f\"[Epoch {epoch+1}/{epochs}] Train Loss: {train_loss:.4f}, Train Acc: {train_accuracy:.2f}%\")\n",
    "        print(f\"[Epoch {epoch+1}/{epochs}] Val Loss: {val_loss:.4f}, Val Acc: {val_accuracy:.2f}%\")\n",
    "        \n",
    "        # 保存最佳模型\n",
    "        if val_accuracy > best_val_acc:\n",
    "            best_val_acc = val_accuracy\n",
    "            best_model = model.state_dict()\n",
    "            counter = 0\n",
    "        else:\n",
    "            counter += 1\n",
    "            if counter >= patience:\n",
    "                print(f\"早停触发，最佳验证准确率: {best_val_acc:.2f}%\")\n",
    "                break\n",
    "    \n",
    "    # 加载最佳模型\n",
    "    model.load_state_dict(best_model)\n",
    "    print(f\"训练完成，最佳验证准确率: {best_val_acc:.2f}%\")\n",
    "    \n",
    "    return model, {\n",
    "        \"train_loss\": train_losses,\n",
    "        \"val_loss\": val_losses,\n",
    "        \"train_acc\": train_acc,\n",
    "        \"val_acc\": val_acc,\n",
    "        \"epochs\": epoch+1\n",
    "    }\n",
    "\n",
    "def test_model(model, testloader):\n",
    "    model.eval()\n",
    "    correct = 0\n",
    "    total = 0\n",
    "    all_preds = []\n",
    "    all_labels = []\n",
    "    \n",
    "    with torch.no_grad():\n",
    "        for images, labels in testloader:\n",
    "            images, labels = images.to(device), labels.to(device)\n",
    "            outputs = model(images)\n",
    "            _, predicted = torch.max(outputs.data, 1)\n",
    "            total += labels.size(0)\n",
    "            correct += (predicted == labels).sum().item()\n",
    "            \n",
    "            all_preds.extend(predicted.cpu().numpy())\n",
    "            all_labels.extend(labels.cpu().numpy())\n",
    "    \n",
    "    acc = 100 * correct / total\n",
    "    print(f\"测试准确率: {acc:.2f}%\")\n",
    "    \n",
    "    # 计算混淆矩阵\n",
    "    conf_matrix = confusion_matrix(all_labels, all_preds)\n",
    "    class_names = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')\n",
    "    \n",
    "    return acc, conf_matrix, all_preds, all_labels, class_names\n",
    "\n",
    "# =================== 可视化函数 ===================\n",
    "\n",
    "def plot_training_history(history, model_name):\n",
    "    epochs = history[\"epochs\"]\n",
    "    plt.figure(figsize=(12, 4))\n",
    "    \n",
    "    # 损失曲线\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.plot(history[\"train_loss\"], label=\"Train Loss\")\n",
    "    plt.plot(history[\"val_loss\"], label=\"Val Loss\")\n",
    "    plt.title(f\"{model_name} 损失曲线\")\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.ylabel(\"Loss\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    \n",
    "    # 准确率曲线\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.plot(history[\"train_acc\"], label=\"Train Accuracy\")\n",
    "    plt.plot(history[\"val_acc\"], label=\"Val Accuracy\")\n",
    "    plt.title(f\"{model_name} 准确率曲线\")\n",
    "    plt.xlabel(\"Epoch\")\n",
    "    plt.ylabel(\"Accuracy (%)\")\n",
    "    plt.legend()\n",
    "    plt.grid(True)\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def plot_confusion_matrix(conf_matrix, class_names, model_name):\n",
    "    plt.figure(figsize=(10, 8))\n",
    "    sns.heatmap(conf_matrix, annot=True, fmt='d', cmap='Blues', \n",
    "                xticklabels=class_names, yticklabels=class_names)\n",
    "    plt.xlabel('预测类别')\n",
    "    plt.ylabel('真实类别')\n",
    "    plt.title(f'{model_name} 混淆矩阵')\n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "\n",
    "def compare_models(results):\n",
    "    model_names = [\"MLP\", \"LSTM\", \"CNN\"]\n",
    "    accuracies = [results[name][\"accuracy\"] for name in model_names]\n",
    "    training_times = [results[name][\"training_time\"] for name in model_names]\n",
    "    \n",
    "    # 准确率对比\n",
    "    plt.figure(figsize=(12, 5))\n",
    "    plt.subplot(1, 2, 1)\n",
    "    plt.bar(model_names, accuracies, color=['blue', 'green', 'red'])\n",
    "    for i, v in enumerate(accuracies):\n",
    "        plt.text(i, v + 1, f'{v:.2f}%', ha='center')\n",
    "    plt.title('模型测试准确率对比')\n",
    "    plt.ylabel('准确率 (%)')\n",
    "    plt.ylim(0, 100)\n",
    "    \n",
    "    # 训练时间对比\n",
    "    plt.subplot(1, 2, 2)\n",
    "    plt.bar(model_names, training_times, color=['blue', 'green', 'red'])\n",
    "    for i, v in enumerate(training_times):\n",
    "        plt.text(i, v + 0.5, f'{v:.2f}s', ha='center')\n",
    "    plt.title('模型训练时间对比')\n",
    "    plt.ylabel('训练时间 (秒)')\n",
    "    \n",
    "    plt.tight_layout()\n",
    "    plt.show()\n",
    "    \n",
    "    # 生成性能表格\n",
    "    results_df = pd.DataFrame({\n",
    "        \"模型\": model_names,\n",
    "        \"测试准确率 (%)\": accuracies,\n",
    "        \"训练时间 (秒)\": training_times\n",
    "    })\n",
    "    print(\"\\n模型性能对比表:\")\n",
    "    print(results_df)\n",
    "\n",
    "# =================== 主流程 ===================\n",
    "\n",
    "if __name__ == \"__main__\":\n",
    "    results = {}\n",
    "    all_conf_matrix = {}\n",
    "    all_preds = {}\n",
    "    all_labels = {}\n",
    "    class_names = None\n",
    "    training_times = {}\n",
    "    \n",
    "    # 记录开始时间\n",
    "    start_time = time.time()\n",
    "    \n",
    "    print(\"\\n Training MLP...\")\n",
    "    mlp_model = MLP()\n",
    "    mlp_start = time.time()\n",
    "    mlp_model, mlp_history = train_model(mlp_model, trainloader, epochs=15)\n",
    "    mlp_results, mlp_conf, mlp_preds, mlp_labels, class_names = test_model(mlp_model, testloader)\n",
    "    mlp_time = time.time() - mlp_start\n",
    "    \n",
    "    results[\"MLP\"] = {\n",
    "        \"accuracy\": mlp_results,\n",
    "        \"history\": mlp_history,\n",
    "        \"training_time\": mlp_time\n",
    "    }\n",
    "    all_conf_matrix[\"MLP\"] = mlp_conf\n",
    "    all_preds[\"MLP\"] = mlp_preds\n",
    "    all_labels[\"MLP\"] = mlp_labels\n",
    "    \n",
    "    print(\"\\n Training LSTM...\")\n",
    "    lstm_model = LSTMClassifier()\n",
    "    lstm_start = time.time()\n",
    "    lstm_model, lstm_history = train_model(lstm_model, trainloader, epochs=15)\n",
    "    lstm_results, lstm_conf, lstm_preds, lstm_labels, _ = test_model(lstm_model, testloader)\n",
    "    lstm_time = time.time() - lstm_start\n",
    "    \n",
    "    results[\"LSTM\"] = {\n",
    "        \"accuracy\": lstm_results,\n",
    "        \"history\": lstm_history,\n",
    "        \"training_time\": lstm_time\n",
    "    }\n",
    "    all_conf_matrix[\"LSTM\"] = lstm_conf\n",
    "    all_preds[\"LSTM\"] = lstm_preds\n",
    "    all_labels[\"LSTM\"] = lstm_labels\n",
    "    \n",
    "    print(\"\\n Training CNN...\")\n",
    "    cnn_model = SimpleCNN()\n",
    "    cnn_start = time.time()\n",
    "    cnn_model, cnn_history = train_model(cnn_model, trainloader, epochs=15)\n",
    "    cnn_results, cnn_conf, cnn_preds, cnn_labels, _ = test_model(cnn_model, testloader)\n",
    "    cnn_time = time.time() - cnn_start\n",
    "    \n",
    "    results[\"CNN\"] = {\n",
    "        \"accuracy\": cnn_results,\n",
    "        \"history\": cnn_history,\n",
    "        \"training_time\": cnn_time\n",
    "    }\n",
    "    all_conf_matrix[\"CNN\"] = cnn_conf\n",
    "    all_preds[\"CNN\"] = cnn_preds\n",
    "    all_labels[\"CNN\"] = cnn_labels\n",
    "    \n",
    "    total_time = time.time() - start_time\n",
    "    print(f\"\\n所有模型训练完成，总耗时: {total_time:.2f} 秒\")\n",
    "    \n",
    "    # 可视化各模型训练历史\n",
    "    plot_training_history(mlp_history, \"MLP\")\n",
    "    plot_training_history(lstm_history, \"LSTM\")\n",
    "    plot_training_history(cnn_history, \"CNN\")\n",
    "    \n",
    "    # 可视化各模型混淆矩阵\n",
    "    for model_name in [\"MLP\", \"LSTM\", \"CNN\"]:\n",
    "        plot_confusion_matrix(all_conf_matrix[model_name], class_names, model_name)\n",
    "    \n",
    "    # 模型性能对比\n",
    "    compare_models(results)\n",
    "    \n",
    "    # 输出分类报告\n",
    "    print(\"\\nCNN模型分类报告:\")\n",
    "    print(classification_report(all_labels[\"CNN\"], all_preds[\"CNN\"], target_names=class_names))"
   ],
   "id": "ffc51c817becc147",
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Using device: cuda\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x400 with 8 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      " Training MLP...\n",
      "训练集大小: 40000, 验证集大小: 10000\n",
      "[Epoch 1/15] Train Loss: 1.7717, Train Acc: 36.85%\n",
      "[Epoch 1/15] Val Loss: 1.6152, Val Acc: 42.37%\n",
      "[Epoch 2/15] Train Loss: 1.6214, Train Acc: 42.46%\n",
      "[Epoch 2/15] Val Loss: 1.5368, Val Acc: 45.56%\n",
      "[Epoch 3/15] Train Loss: 1.5555, Train Acc: 44.50%\n",
      "[Epoch 3/15] Val Loss: 1.4916, Val Acc: 47.45%\n",
      "[Epoch 4/15] Train Loss: 1.5097, Train Acc: 46.56%\n",
      "[Epoch 4/15] Val Loss: 1.4707, Val Acc: 47.95%\n",
      "[Epoch 5/15] Train Loss: 1.4765, Train Acc: 47.69%\n",
      "[Epoch 5/15] Val Loss: 1.4556, Val Acc: 48.29%\n",
      "[Epoch 6/15] Train Loss: 1.4474, Train Acc: 48.60%\n",
      "[Epoch 6/15] Val Loss: 1.4238, Val Acc: 50.03%\n",
      "[Epoch 7/15] Train Loss: 1.4175, Train Acc: 49.80%\n",
      "[Epoch 7/15] Val Loss: 1.4130, Val Acc: 49.97%\n",
      "[Epoch 8/15] Train Loss: 1.3857, Train Acc: 50.88%\n",
      "[Epoch 8/15] Val Loss: 1.4060, Val Acc: 50.24%\n",
      "[Epoch 9/15] Train Loss: 1.3653, Train Acc: 51.70%\n",
      "[Epoch 9/15] Val Loss: 1.3930, Val Acc: 50.80%\n",
      "[Epoch 10/15] Train Loss: 1.3511, Train Acc: 52.27%\n",
      "[Epoch 10/15] Val Loss: 1.3819, Val Acc: 51.20%\n",
      "[Epoch 11/15] Train Loss: 1.3192, Train Acc: 52.97%\n",
      "[Epoch 11/15] Val Loss: 1.3786, Val Acc: 51.97%\n",
      "[Epoch 12/15] Train Loss: 1.3132, Train Acc: 53.53%\n",
      "[Epoch 12/15] Val Loss: 1.3861, Val Acc: 51.04%\n",
      "[Epoch 13/15] Train Loss: 1.2947, Train Acc: 54.33%\n",
      "[Epoch 13/15] Val Loss: 1.3646, Val Acc: 51.39%\n",
      "[Epoch 14/15] Train Loss: 1.2834, Train Acc: 54.65%\n",
      "[Epoch 14/15] Val Loss: 1.3596, Val Acc: 52.15%\n",
      "[Epoch 15/15] Train Loss: 1.2559, Train Acc: 55.37%\n",
      "[Epoch 15/15] Val Loss: 1.3561, Val Acc: 52.07%\n",
      "训练完成，最佳验证准确率: 52.15%\n",
      "测试准确率: 52.02%\n",
      "\n",
      " Training LSTM...\n",
      "训练集大小: 40000, 验证集大小: 10000\n",
      "[Epoch 1/15] Train Loss: 1.8173, Train Acc: 33.68%\n",
      "[Epoch 1/15] Val Loss: 1.6398, Val Acc: 41.02%\n",
      "[Epoch 2/15] Train Loss: 1.5658, Train Acc: 42.84%\n",
      "[Epoch 2/15] Val Loss: 1.5320, Val Acc: 44.87%\n",
      "[Epoch 3/15] Train Loss: 1.4514, Train Acc: 47.48%\n",
      "[Epoch 3/15] Val Loss: 1.4351, Val Acc: 48.18%\n",
      "[Epoch 4/15] Train Loss: 1.3607, Train Acc: 50.76%\n",
      "[Epoch 4/15] Val Loss: 1.3968, Val Acc: 49.94%\n",
      "[Epoch 5/15] Train Loss: 1.2883, Train Acc: 53.26%\n",
      "[Epoch 5/15] Val Loss: 1.3512, Val Acc: 51.74%\n",
      "[Epoch 6/15] Train Loss: 1.2172, Train Acc: 56.27%\n",
      "[Epoch 6/15] Val Loss: 1.3232, Val Acc: 53.01%\n",
      "[Epoch 7/15] Train Loss: 1.1588, Train Acc: 57.87%\n",
      "[Epoch 7/15] Val Loss: 1.3225, Val Acc: 53.36%\n",
      "[Epoch 8/15] Train Loss: 1.1039, Train Acc: 60.43%\n",
      "[Epoch 8/15] Val Loss: 1.2991, Val Acc: 54.08%\n",
      "[Epoch 9/15] Train Loss: 1.0452, Train Acc: 62.24%\n",
      "[Epoch 9/15] Val Loss: 1.3213, Val Acc: 53.74%\n",
      "[Epoch 10/15] Train Loss: 0.9916, Train Acc: 64.06%\n",
      "[Epoch 10/15] Val Loss: 1.3134, Val Acc: 54.95%\n",
      "[Epoch 11/15] Train Loss: 0.9414, Train Acc: 66.29%\n",
      "[Epoch 11/15] Val Loss: 1.3439, Val Acc: 53.85%\n",
      "[Epoch 12/15] Train Loss: 0.8938, Train Acc: 67.89%\n",
      "[Epoch 12/15] Val Loss: 1.3362, Val Acc: 54.76%\n",
      "[Epoch 13/15] Train Loss: 0.8429, Train Acc: 69.86%\n",
      "[Epoch 13/15] Val Loss: 1.3591, Val Acc: 55.19%\n",
      "[Epoch 14/15] Train Loss: 0.8018, Train Acc: 71.04%\n",
      "[Epoch 14/15] Val Loss: 1.3736, Val Acc: 54.86%\n",
      "[Epoch 15/15] Train Loss: 0.7607, Train Acc: 72.83%\n",
      "[Epoch 15/15] Val Loss: 1.4062, Val Acc: 55.11%\n",
      "训练完成，最佳验证准确率: 55.19%\n",
      "测试准确率: 54.81%\n",
      "\n",
      " Training CNN...\n",
      "训练集大小: 40000, 验证集大小: 10000\n",
      "[Epoch 1/15] Train Loss: 1.5032, Train Acc: 45.34%\n",
      "[Epoch 1/15] Val Loss: 1.2102, Val Acc: 55.68%\n",
      "[Epoch 2/15] Train Loss: 1.1682, Train Acc: 58.08%\n",
      "[Epoch 2/15] Val Loss: 1.0311, Val Acc: 63.06%\n",
      "[Epoch 3/15] Train Loss: 1.0116, Train Acc: 64.33%\n",
      "[Epoch 3/15] Val Loss: 0.9478, Val Acc: 66.30%\n",
      "[Epoch 4/15] Train Loss: 0.9082, Train Acc: 67.68%\n",
      "[Epoch 4/15] Val Loss: 0.8821, Val Acc: 68.94%\n",
      "[Epoch 5/15] Train Loss: 0.8210, Train Acc: 71.19%\n",
      "[Epoch 5/15] Val Loss: 0.8540, Val Acc: 70.07%\n",
      "[Epoch 6/15] Train Loss: 0.7556, Train Acc: 73.26%\n",
      "[Epoch 6/15] Val Loss: 0.8364, Val Acc: 70.86%\n",
      "[Epoch 7/15] Train Loss: 0.6893, Train Acc: 75.52%\n",
      "[Epoch 7/15] Val Loss: 0.8395, Val Acc: 70.91%\n",
      "[Epoch 8/15] Train Loss: 0.6283, Train Acc: 77.46%\n",
      "[Epoch 8/15] Val Loss: 0.8612, Val Acc: 71.07%\n",
      "[Epoch 9/15] Train Loss: 0.5806, Train Acc: 79.17%\n",
      "[Epoch 9/15] Val Loss: 0.8429, Val Acc: 71.91%\n",
      "[Epoch 10/15] Train Loss: 0.5301, Train Acc: 80.84%\n",
      "[Epoch 10/15] Val Loss: 0.8247, Val Acc: 72.86%\n",
      "[Epoch 11/15] Train Loss: 0.4834, Train Acc: 82.75%\n",
      "[Epoch 11/15] Val Loss: 0.8642, Val Acc: 71.91%\n",
      "[Epoch 12/15] Train Loss: 0.4499, Train Acc: 83.74%\n",
      "[Epoch 12/15] Val Loss: 0.8767, Val Acc: 72.77%\n",
      "[Epoch 13/15] Train Loss: 0.4199, Train Acc: 84.49%\n",
      "[Epoch 13/15] Val Loss: 0.9315, Val Acc: 72.12%\n",
      "早停触发，最佳验证准确率: 72.86%\n",
      "训练完成，最佳验证准确率: 72.86%\n",
      "测试准确率: 72.45%\n",
      "\n",
      "所有模型训练完成，总耗时: 302.91 秒\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:276: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:276: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:276: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:276: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:276: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:276: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:276: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 25439 (\\N{CJK UNIFIED IDEOGRAPH-635F}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22833 (\\N{CJK UNIFIED IDEOGRAPH-5931}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26354 (\\N{CJK UNIFIED IDEOGRAPH-66F2}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32447 (\\N{CJK UNIFIED IDEOGRAPH-7EBF}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x400 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 31867 (\\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 21035 (\\N{CJK UNIFIED IDEOGRAPH-522B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31867 (\\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21035 (\\N{CJK UNIFIED IDEOGRAPH-522B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 31867 (\\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 21035 (\\N{CJK UNIFIED IDEOGRAPH-522B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31867 (\\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21035 (\\N{CJK UNIFIED IDEOGRAPH-522B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 31867 (\\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 21035 (\\N{CJK UNIFIED IDEOGRAPH-522B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:286: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 39044 (\\N{CJK UNIFIED IDEOGRAPH-9884}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31867 (\\N{CJK UNIFIED IDEOGRAPH-7C7B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 21035 (\\N{CJK UNIFIED IDEOGRAPH-522B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30495 (\\N{CJK UNIFIED IDEOGRAPH-771F}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23454 (\\N{CJK UNIFIED IDEOGRAPH-5B9E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28151 (\\N{CJK UNIFIED IDEOGRAPH-6DF7}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 28102 (\\N{CJK UNIFIED IDEOGRAPH-6DC6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30697 (\\N{CJK UNIFIED IDEOGRAPH-77E9}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38453 (\\N{CJK UNIFIED IDEOGRAPH-9635}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x800 with 2 Axes>"
      ],
      "image/png": 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"
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 27979 (\\N{CJK UNIFIED IDEOGRAPH-6D4B}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 35797 (\\N{CJK UNIFIED IDEOGRAPH-8BD5}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 23545 (\\N{CJK UNIFIED IDEOGRAPH-5BF9}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Local\\Temp\\ipykernel_11456\\3657861372.py:312: UserWarning: Glyph 31186 (\\N{CJK UNIFIED IDEOGRAPH-79D2}) missing from font(s) Arial.\n",
      "  plt.tight_layout()\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 20934 (\\N{CJK UNIFIED IDEOGRAPH-51C6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 30830 (\\N{CJK UNIFIED IDEOGRAPH-786E}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 29575 (\\N{CJK UNIFIED IDEOGRAPH-7387}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27169 (\\N{CJK UNIFIED IDEOGRAPH-6A21}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 22411 (\\N{CJK UNIFIED IDEOGRAPH-578B}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35797 (\\N{CJK UNIFIED IDEOGRAPH-8BD5}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 23545 (\\N{CJK UNIFIED IDEOGRAPH-5BF9}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 27604 (\\N{CJK UNIFIED IDEOGRAPH-6BD4}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 35757 (\\N{CJK UNIFIED IDEOGRAPH-8BAD}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 32451 (\\N{CJK UNIFIED IDEOGRAPH-7EC3}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 26102 (\\N{CJK UNIFIED IDEOGRAPH-65F6}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 38388 (\\N{CJK UNIFIED IDEOGRAPH-95F4}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n",
      "C:\\Users\\30979\\AppData\\Roaming\\Python\\Python312\\site-packages\\IPython\\core\\pylabtools.py:170: UserWarning: Glyph 31186 (\\N{CJK UNIFIED IDEOGRAPH-79D2}) missing from font(s) Arial.\n",
      "  fig.canvas.print_figure(bytes_io, **kw)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1200x500 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "模型性能对比表:\n",
      "     模型  测试准确率 (%)    训练时间 (秒)\n",
      "0   MLP      52.02   96.984030\n",
      "1  LSTM      54.81  111.763231\n",
      "2   CNN      72.45   94.149981\n",
      "\n",
      "CNN模型分类报告:\n",
      "              precision    recall  f1-score   support\n",
      "\n",
      "       plane       0.77      0.76      0.76      1000\n",
      "         car       0.84      0.83      0.84      1000\n",
      "        bird       0.60      0.61      0.61      1000\n",
      "         cat       0.52      0.64      0.57      1000\n",
      "        deer       0.74      0.58      0.65      1000\n",
      "         dog       0.65      0.56      0.60      1000\n",
      "        frog       0.71      0.86      0.78      1000\n",
      "       horse       0.82      0.76      0.79      1000\n",
      "        ship       0.86      0.83      0.84      1000\n",
      "       truck       0.80      0.83      0.81      1000\n",
      "\n",
      "    accuracy                           0.72     10000\n",
      "   macro avg       0.73      0.72      0.72     10000\n",
      "weighted avg       0.73      0.72      0.72     10000\n",
      "\n"
     ]
    }
   ],
   "execution_count": 5
  }
 ],
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   "file_extension": ".py",
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   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
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